ASA 127th Meeting M.I.T. 1994 June 6-10

4pMU7. Classification of music type by a multilayer neural network.

Western people who are exposed to different types music, such as pop or
classic, can easily distinguish between them after listening to any composition
for a few seconds. It is still unclear what the features are in the music that
allow people this quick recognition. In the present study a model is proposed
that distinguishes between two classes of music, pop and classic. The model is
a decision making system that was implemented by integrating outputs of a
multilayer neural network (NN). The input nodes to the NN were obtained by a
preprocessing algorithm that included the following steps: (1) Dividing the
musical composition to intervals of 16.8 ms; (2) applying spectral analysis on
each interval; (3) combining the spectral components of T successive intervals
into F divisions, which were obtained by dividing the logarithmic of the
audiometric frequency range into equal F parts. As a result of the
preprocessing algorithm interval of T*16.8 ms of musical signal was presented
by F*T input values to the NN. Our results show that it is enough to train the
NN with a group of short intervals (i.e., 0.3 s), and to represent its spectrum
by about 20 values in order to obtain a 100% success in distinguishing between
two types of music.